Bonus Task | Visualizations

by: Yana Kryshchuk

import altair as alt
import pandas as pd
from vega_datasets import data

alt.data_transformers.disable_max_rows()

DataTransformerRegistry.enable('default')

data_ = pd.read_csv("all_month.csv")
# Filtering the earthquakes data
data_ = data_[data_["type"] == "earthquake"]
data_["index_"] = data_.index

data_["time"] = pd.to_datetime(data_["time"])
data_["day_of_the_week"] = data_["time"].dt.day_name()

data_["month"] = data_["time"].dt.month_name()

1. Distribution of the earthquakes by magnitude

For this kind of insight I'm using a layered binned histogram as it allows us to see a better picture at the data and its quantitative patterns.
Even though in February we had significantly smaller amount of data than in January, we are able to analize the difference in magnitude distributions.

alt.Chart(data_).mark_area(opacity=0.5, interpolate='step').encode(
    x=alt.X('mag:Q', bin=alt.Bin(maxbins=25), title="Magnitude"),
    y=alt.Y('count()', stack=None, title="No. of earthquakes"),
    color=alt.Color("month:N", scale=alt.Scale(scheme='greenblue'), sort=["January"], title="Month"),
    tooltip=[alt.Tooltip("month", title="Month"),
#              alt.Tooltip("mag:Q", title="Magnitude"),
            alt.Tooltip('count()', title='No. of earthquakes')]
).properties(width = 600, height = 500, title = "Magnitude distribution" )

2. Changes in the number of earthquakes throughout the months

For the analysis of the time-series data I used both standard line plots and heatmaps as they are the most suitable ones considering the type of data we have

alt.Chart(data_).mark_line(color="green", point=alt.OverlayMarkDef(color="green")).encode(
    x=alt.X('yearmonthdate(time):O', title="Date"),
    y=alt.Y('count():Q', title="No. of earthquakes"),
    tooltip=[alt.Tooltip('count()', title='No. of earthquakes'),
            alt.Tooltip("yearmonthdate(time)", title="Date")]
).properties(width=600, height=500, title="Changes in the number of earthquakes throughout the months")

alt.Chart(data_).mark_rect().encode(
    x=alt.X('hours(time):O', title="Time of the day"),
    y=alt.Y('day(time):O', title="Day of the week"),
    color=alt.Color('count():Q', title="No. of earthquakes", scale=alt.Scale(scheme='greenblue')),
    tooltip=[alt.Tooltip("count()", title="No. of earthquakes"),
            alt.Tooltip("hours(time)", title="Time of the day"),
            alt.Tooltip("day(time)", title="Day of the week")]
).properties(width=600, height=500, title="Changes in the number of earthquakes throughout the day")

3. Geographical location of the earthquakes

For this kind of analysis it's better to use the world map, as we have location-based data.
The issue with such way of the visualization is inability to zoom in the map and get a more detailed look at the map. Ideally in that scenario we'd use a cloropleth map, as it allows to group the points in regions

countries = alt.topo_feature(data.world_110m.url, 'countries')

base = alt.Chart(countries).mark_geoshape(
    fill='lightgray',
    stroke='white'
).project('equirectangular').properties(
    width=1000,
    height=500
)

earthquakes = alt.Chart(data_).mark_circle(color="green").encode(
    longitude='longitude:Q',
    latitude='latitude:Q',
    size=alt.value(10),
    tooltip=[alt.Tooltip("count()", title="No. of earthquakes")]
).properties(width=1000,
    height=500)

(base + earthquakes).properties(width=1000, height=500, title="Geographical location of the earthquakes")